Recent advances in neural implicit surfaces for multi-view 3D reconstruction primarily focus on improving large-scale surface reconstruction accuracy, but often produce over-smoothed geometries that lack fine surface details. To address this, we present High-Resolution NeuS (HR-NeuS), a novel neural implicit surface reconstruction method that recovers high-frequency surface geometry while maintaining large-scale reconstruction accuracy. We achieve this by utilizing (i) multi-resolution hash grid encoding rather than positional encoding at high frequencies, which boosts our model's expressiveness of local geometry details; (ii) a coarse-to-fine algorithmic framework that selectively applies surface regularization to coarse geometry without smoothing away fine details; (iii) a coarse-to-fine grid annealing strategy to train the network. We demonstrate through experiments on DTU and BlendedMVS datasets that our approach produces 3D geometries that are qualitatively more detailed and quantitatively of similar accuracy compared to previous approaches.

翻译：近年来，基于神经隐式表面的多视图三维重建技术主要聚焦于提升大尺度表面重建精度，但往往产生过度平滑的几何结构，缺乏精细的表面细节。为解决这一问题，我们提出高分辨率NeuS（HR-NeuS），一种新颖的神经隐式表面重建方法，能够在保持大尺度重建精度的同时恢复高频表面几何。我们通过以下手段实现这一目标：（i）在高频区域采用多分辨率哈希网格编码替代位置编码，从而增强模型对局部几何细节的表达能力；（ii）设计粗到细的算法框架，有选择地对粗几何施加表面正则化，避免平滑掉精细细节；（iii）采用粗到细的网格退火策略训练网络。通过在DTU和BlendedMVS数据集上的实验证明，与先前方法相比，我们的方法生成的3D几何在质量上细节更丰富，在精度上达到相近水平。